On the role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas

Lorenzo Bruzzone; Michele Dalponte; Damiano Gianelle

doi:10.1117/12.739216

24 October 2007 On the role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas

Lorenzo Bruzzone, Michele Dalponte, Damiano Gianelle

Author Affiliations +

Proceedings Volume 6748, Image and Signal Processing for Remote Sensing XIII; 67480C (2007) https://doi.org/10.1117/12.739216
Event: SPIE Remote Sensing, 2007, Florence, Italy

Abstract

Remote sensing hyperspectral sensors are important and powerful instruments for addressing land-cover classification problems, as they permit a detailed characterization of the spectral behavior of the considered information classes. However, the processing of hyperspectral data is particularly complex both from the theoretical viewpoint (e.g. problems related to the Hughes phenomenon [1]) and from the computational perspective. In this context, despite many investigations have been presented in the literature on feature reduction and feature extraction in hyperspectral data, only few studies analyzed the role of the spectral resolution on the classification accuracy in different application domains. In this paper, we present an empirical study aimed at understanding the relationships among spectral resolution, classifier complexity, and classification accuracy obtained with hyperspectral sensors in classification of forest areas. On the basis of this study, important conclusions can be derived on the choice of the spectral resolution of hyperspectral sensors for forest applications, also in relation to the complexity of the adopted classification methodology. These conclusions can be exploited both in the context of the design of hyperspectral sensors (or for programming spectral channels of the available instruments) and in the phase of development of classification system for hyperspectral data.

Citation Download Citation

Lorenzo Bruzzone, Michele Dalponte, and Damiano Gianelle "On the role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas", Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67480C (24 October 2007); https://doi.org/10.1117/12.739216

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Spectral resolution

Sensors

Classification systems

Feature selection

Hyperspectral imaging

Remote sensing

Algorithm development

Show All Keywords

Keywords/Phrases

Search In:

Publication Years